Overview

Dataset statistics

Number of variables14
Number of observations2196
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory474.9 KiB
Average record size in memory221.4 B

Variable types

Categorical2
Numeric12

Alerts

Hb is highly overall correlated with SexHigh correlation
RBC is highly overall correlated with MCHCHigh correlation
MCHC is highly overall correlated with RBCHigh correlation
hbA is highly overall correlated with Thalassemia TypeHigh correlation
hbA2 is highly overall correlated with Thalassemia TypeHigh correlation
Sex is highly overall correlated with HbHigh correlation
Thalassemia Type is highly overall correlated with hbA and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-10-22 09:51:03.601831
Analysis finished2023-10-22 09:51:18.654452
Duration15.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.5 KiB
M
1268 
F
928 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2196
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 1268
57.7%
F 928
42.3%

Length

2023-10-22T15:51:18.725020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T15:51:18.825574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 1268
57.7%
f 928
42.3%

Most occurring characters

ValueCountFrequency (%)
M 1268
57.7%
F 928
42.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2196
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1268
57.7%
F 928
42.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2196
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1268
57.7%
F 928
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1268
57.7%
F 928
42.3%

Age
Real number (ℝ)

Distinct1593
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.46393
Minimum4.55
Maximum49.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:18.947172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.55
5-th percentile5.6975
Q110.0975
median19.135
Q327.5525
95-th percentile42.7925
Maximum49.91
Range45.36
Interquartile range (IQR)17.455

Descriptive statistics

Standard deviation11.466336
Coefficient of variation (CV)0.56031933
Kurtosis-0.57523925
Mean20.46393
Median Absolute Deviation (MAD)8.77
Skewness0.56689914
Sum44938.79
Variance131.47685
MonotonicityNot monotonic
2023-10-22T15:51:19.104946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.16 6
 
0.3%
8.03 5
 
0.2%
22.99 5
 
0.2%
20.54 5
 
0.2%
12.5 4
 
0.2%
7.03 4
 
0.2%
5.03 4
 
0.2%
6.89 4
 
0.2%
10.07 4
 
0.2%
12.62 4
 
0.2%
Other values (1583) 2151
98.0%
ValueCountFrequency (%)
4.55 3
0.1%
4.56 1
 
< 0.1%
4.59 1
 
< 0.1%
4.6 2
0.1%
4.61 1
 
< 0.1%
4.64 1
 
< 0.1%
4.65 2
0.1%
4.68 1
 
< 0.1%
4.69 3
0.1%
4.71 2
0.1%
ValueCountFrequency (%)
49.91 1
< 0.1%
49.89 1
< 0.1%
49.87 1
< 0.1%
49.84 1
< 0.1%
49.7 1
< 0.1%
49.52 1
< 0.1%
49.49 1
< 0.1%
49.47 1
< 0.1%
48.84 1
< 0.1%
48.77 1
< 0.1%

Hb
Real number (ℝ)

HIGH CORRELATION 

Distinct298
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5862158
Minimum7.01
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:19.255471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.01
5-th percentile7.26
Q18.07
median8.57
Q39.14
95-th percentile9.87
Maximum10
Range2.99
Interquartile range (IQR)1.07

Descriptive statistics

Standard deviation0.76809982
Coefficient of variation (CV)0.089457316
Kurtosis-0.77498795
Mean8.5862158
Median Absolute Deviation (MAD)0.53
Skewness-0.032283554
Sum18855.33
Variance0.58997734
MonotonicityNot monotonic
2023-10-22T15:51:19.409370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 20
 
0.9%
8.24 19
 
0.9%
8.17 19
 
0.9%
8.09 18
 
0.8%
8.5 16
 
0.7%
8.28 16
 
0.7%
8.89 16
 
0.7%
8.27 15
 
0.7%
8.87 15
 
0.7%
8.11 15
 
0.7%
Other values (288) 2027
92.3%
ValueCountFrequency (%)
7.01 4
0.2%
7.02 3
 
0.1%
7.03 6
0.3%
7.04 3
 
0.1%
7.05 3
 
0.1%
7.06 5
0.2%
7.07 8
0.4%
7.08 5
0.2%
7.09 6
0.3%
7.1 2
 
0.1%
ValueCountFrequency (%)
10 5
 
0.2%
9.99 14
0.6%
9.98 10
0.5%
9.97 7
0.3%
9.96 10
0.5%
9.95 10
0.5%
9.94 7
0.3%
9.93 11
0.5%
9.92 1
 
< 0.1%
9.91 11
0.5%

PCV
Real number (ℝ)

Distinct1068
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.864677
Minimum20.18
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:19.550920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.18
5-th percentile23.3775
Q127.03
median30.07
Q333.0225
95-th percentile35.3425
Maximum36
Range15.82
Interquartile range (IQR)5.9925

Descriptive statistics

Standard deviation3.7236334
Coefficient of variation (CV)0.12468353
Kurtosis-0.81862395
Mean29.864677
Median Absolute Deviation (MAD)2.99
Skewness-0.27639038
Sum65582.83
Variance13.865445
MonotonicityNot monotonic
2023-10-22T15:51:19.699001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.24 7
 
0.3%
32.38 7
 
0.3%
32.22 7
 
0.3%
25.8 7
 
0.3%
25.67 6
 
0.3%
27.8 6
 
0.3%
32.16 6
 
0.3%
29.36 6
 
0.3%
32.71 6
 
0.3%
30.69 6
 
0.3%
Other values (1058) 2132
97.1%
ValueCountFrequency (%)
20.18 1
< 0.1%
20.21 1
< 0.1%
20.32 1
< 0.1%
20.34 1
< 0.1%
20.4 1
< 0.1%
20.48 1
< 0.1%
20.68 1
< 0.1%
20.69 1
< 0.1%
20.75 1
< 0.1%
20.85 1
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
35.99 1
 
< 0.1%
35.98 1
 
< 0.1%
35.96 1
 
< 0.1%
35.95 4
0.2%
35.94 1
 
< 0.1%
35.9 2
0.1%
35.89 3
0.1%
35.88 1
 
< 0.1%
35.87 1
 
< 0.1%

RBC
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)13.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9847745
Minimum1.5
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:19.840227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.647
Q12.22
median3
Q33.725
95-th percentile4.34
Maximum4.5
Range3
Interquartile range (IQR)1.505

Descriptive statistics

Standard deviation0.86763332
Coefficient of variation (CV)0.29068639
Kurtosis-1.2197633
Mean2.9847745
Median Absolute Deviation (MAD)0.75
Skewness-0.0033834597
Sum6551.58
Variance0.75278759
MonotonicityNot monotonic
2023-10-22T15:51:19.978149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 15
 
0.7%
1.98 15
 
0.7%
2.59 15
 
0.7%
3.42 14
 
0.6%
3 13
 
0.6%
3.84 13
 
0.6%
1.91 13
 
0.6%
3.2 13
 
0.6%
3.24 12
 
0.5%
3.11 12
 
0.5%
Other values (291) 2060
93.8%
ValueCountFrequency (%)
1.5 2
 
0.1%
1.51 6
0.3%
1.52 6
0.3%
1.53 9
0.4%
1.54 7
0.3%
1.55 7
0.3%
1.56 10
0.5%
1.57 10
0.5%
1.58 7
0.3%
1.59 7
0.3%
ValueCountFrequency (%)
4.5 5
0.2%
4.49 6
0.3%
4.48 4
0.2%
4.47 9
0.4%
4.46 4
0.2%
4.45 4
0.2%
4.44 8
0.4%
4.43 6
0.3%
4.42 4
0.2%
4.41 6
0.3%

MCV
Real number (ℝ)

Distinct1793
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.952691
Minimum22.58
Maximum84.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:20.117591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.58
5-th percentile33.5875
Q149.925
median62.42
Q373.7825
95-th percentile82.915
Maximum84.96
Range62.38
Interquartile range (IQR)23.8575

Descriptive statistics

Standard deviation15.410262
Coefficient of variation (CV)0.25282332
Kurtosis-0.83839592
Mean60.952691
Median Absolute Deviation (MAD)11.825
Skewness-0.3522209
Sum133852.11
Variance237.47616
MonotonicityNot monotonic
2023-10-22T15:51:20.257161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.18 5
 
0.2%
59.39 4
 
0.2%
72.65 4
 
0.2%
52.79 3
 
0.1%
74.58 3
 
0.1%
84.63 3
 
0.1%
71.36 3
 
0.1%
76.23 3
 
0.1%
73.77 3
 
0.1%
52.52 3
 
0.1%
Other values (1783) 2162
98.5%
ValueCountFrequency (%)
22.58 1
< 0.1%
22.79 1
< 0.1%
23.99 1
< 0.1%
24.32 1
< 0.1%
24.61 1
< 0.1%
24.85 1
< 0.1%
25.14 1
< 0.1%
25.25 1
< 0.1%
25.44 1
< 0.1%
25.51 1
< 0.1%
ValueCountFrequency (%)
84.96 1
< 0.1%
84.91 1
< 0.1%
84.89 1
< 0.1%
84.88 1
< 0.1%
84.85 1
< 0.1%
84.84 2
0.1%
84.83 1
< 0.1%
84.81 1
< 0.1%
84.8 1
< 0.1%
84.77 1
< 0.1%

MCH
Real number (ℝ)

Distinct954
Distinct (%)43.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.352756
Minimum16.01
Maximum26.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:20.397366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.01
5-th percentile16.567
Q118.56
median21.33
Q324.07
95-th percentile26.416
Maximum26.99
Range10.98
Interquartile range (IQR)5.51

Descriptive statistics

Standard deviation3.1699432
Coefficient of variation (CV)0.14845592
Kurtosis-1.220649
Mean21.352756
Median Absolute Deviation (MAD)2.76
Skewness0.06658254
Sum46869.3
Variance10.04854
MonotonicityNot monotonic
2023-10-22T15:51:20.539934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.58 8
 
0.4%
18.94 7
 
0.3%
17.66 7
 
0.3%
24.01 7
 
0.3%
19.72 6
 
0.3%
25.28 6
 
0.3%
20.46 6
 
0.3%
16.6 6
 
0.3%
18.15 6
 
0.3%
25.22 6
 
0.3%
Other values (944) 2130
97.0%
ValueCountFrequency (%)
16.01 1
 
< 0.1%
16.02 2
 
0.1%
16.03 2
 
0.1%
16.04 3
0.1%
16.05 2
 
0.1%
16.06 1
 
< 0.1%
16.07 6
0.3%
16.08 3
0.1%
16.09 1
 
< 0.1%
16.1 1
 
< 0.1%
ValueCountFrequency (%)
26.99 1
 
< 0.1%
26.98 1
 
< 0.1%
26.97 2
0.1%
26.96 3
0.1%
26.95 2
0.1%
26.94 4
0.2%
26.93 1
 
< 0.1%
26.92 3
0.1%
26.91 4
0.2%
26.89 3
0.1%

MCHC
Real number (ℝ)

HIGH CORRELATION 

Distinct1444
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.576662
Minimum16.03
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:20.688499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.03
5-th percentile18.3475
Q121.94
median27.655
Q333.395
95-th percentile43.23
Maximum60
Range43.97
Interquartile range (IQR)11.455

Descriptive statistics

Standard deviation7.8988722
Coefficient of variation (CV)0.2764099
Kurtosis0.49851806
Mean28.576662
Median Absolute Deviation (MAD)5.725
Skewness0.79270202
Sum62754.35
Variance62.392182
MonotonicityNot monotonic
2023-10-22T15:51:20.833487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.24 7
 
0.3%
33.95 5
 
0.2%
32.06 5
 
0.2%
32.17 5
 
0.2%
18.8 5
 
0.2%
25.95 5
 
0.2%
20 5
 
0.2%
32.03 5
 
0.2%
19.05 5
 
0.2%
19.25 4
 
0.2%
Other values (1434) 2145
97.7%
ValueCountFrequency (%)
16.03 1
< 0.1%
16.78 1
< 0.1%
16.86 1
< 0.1%
16.88 1
< 0.1%
16.94 1
< 0.1%
17 1
< 0.1%
17.02 2
0.1%
17.22 1
< 0.1%
17.29 1
< 0.1%
17.32 1
< 0.1%
ValueCountFrequency (%)
60 1
< 0.1%
58.54 1
< 0.1%
57.77 1
< 0.1%
57.2 1
< 0.1%
56.89 1
< 0.1%
56.85 1
< 0.1%
56.49 1
< 0.1%
56.35 1
< 0.1%
56.17 1
< 0.1%
55.77 1
< 0.1%

RDW
Real number (ℝ)

Distinct669
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.510824
Minimum30
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:20.965676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30.34
Q131.75
median33.565
Q335.26
95-th percentile36.6525
Maximum37
Range7
Interquartile range (IQR)3.51

Descriptive statistics

Standard deviation2.014409
Coefficient of variation (CV)0.060112188
Kurtosis-1.1669345
Mean33.510824
Median Absolute Deviation (MAD)1.725
Skewness-0.030327128
Sum73589.77
Variance4.0578435
MonotonicityNot monotonic
2023-10-22T15:51:21.108787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.43 11
 
0.5%
36.57 10
 
0.5%
35.27 10
 
0.5%
30.42 9
 
0.4%
33.8 9
 
0.4%
33.6 8
 
0.4%
31.17 8
 
0.4%
30.37 8
 
0.4%
33.47 8
 
0.4%
33.48 8
 
0.4%
Other values (659) 2107
95.9%
ValueCountFrequency (%)
30 3
0.1%
30.01 3
0.1%
30.02 5
0.2%
30.03 5
0.2%
30.04 2
 
0.1%
30.05 2
 
0.1%
30.06 3
0.1%
30.07 3
0.1%
30.08 2
 
0.1%
30.09 1
 
< 0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
36.99 5
0.2%
36.98 3
0.1%
36.97 5
0.2%
36.96 5
0.2%
36.95 2
 
0.1%
36.94 4
0.2%
36.93 5
0.2%
36.92 2
 
0.1%
36.91 3
0.1%

WBC
Real number (ℝ)

Distinct682
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3946357
Minimum4.9
Maximum11.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:21.376930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile5.26
Q16.65
median8.335
Q310.1525
95-th percentile11.6525
Maximum11.99
Range7.09
Interquartile range (IQR)3.5025

Descriptive statistics

Standard deviation2.0411274
Coefficient of variation (CV)0.24314664
Kurtosis-1.1817815
Mean8.3946357
Median Absolute Deviation (MAD)1.745
Skewness0.048232833
Sum18434.62
Variance4.1662012
MonotonicityNot monotonic
2023-10-22T15:51:21.517047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.02 11
 
0.5%
7.9 10
 
0.5%
10.36 9
 
0.4%
5.82 9
 
0.4%
7.27 9
 
0.4%
6.65 8
 
0.4%
10.71 8
 
0.4%
10.31 8
 
0.4%
11.16 8
 
0.4%
6.82 8
 
0.4%
Other values (672) 2108
96.0%
ValueCountFrequency (%)
4.9 2
 
0.1%
4.91 5
0.2%
4.92 3
0.1%
4.93 3
0.1%
4.94 7
0.3%
4.95 4
0.2%
4.96 2
 
0.1%
4.97 2
 
0.1%
4.98 1
 
< 0.1%
4.99 4
0.2%
ValueCountFrequency (%)
11.99 6
0.3%
11.98 3
0.1%
11.97 6
0.3%
11.95 4
0.2%
11.94 4
0.2%
11.93 6
0.3%
11.92 3
0.1%
11.91 2
 
0.1%
11.9 2
 
0.1%
11.89 1
 
< 0.1%

Plt
Real number (ℝ)

Distinct2137
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.27199
Minimum98.23
Maximum508.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:21.662577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.23
5-th percentile119.8
Q1202.635
median307.41
Q3404.855
95-th percentile488.3025
Maximum508.76
Range410.53
Interquartile range (IQR)202.22

Descriptive statistics

Standard deviation117.73831
Coefficient of variation (CV)0.38568329
Kurtosis-1.1859891
Mean305.27199
Median Absolute Deviation (MAD)101.04
Skewness-0.016783814
Sum670377.3
Variance13862.309
MonotonicityNot monotonic
2023-10-22T15:51:21.798680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
380.5 2
 
0.1%
418.92 2
 
0.1%
121.54 2
 
0.1%
458.49 2
 
0.1%
268.13 2
 
0.1%
416.26 2
 
0.1%
227.12 2
 
0.1%
307.84 2
 
0.1%
110.27 2
 
0.1%
229.78 2
 
0.1%
Other values (2127) 2176
99.1%
ValueCountFrequency (%)
98.23 1
< 0.1%
98.31 1
< 0.1%
98.37 1
< 0.1%
98.4 1
< 0.1%
98.71 1
< 0.1%
98.93 1
< 0.1%
99.02 1
< 0.1%
99.05 1
< 0.1%
99.08 1
< 0.1%
99.53 1
< 0.1%
ValueCountFrequency (%)
508.76 1
< 0.1%
508.71 1
< 0.1%
508.43 1
< 0.1%
507.6 1
< 0.1%
507.51 1
< 0.1%
507.47 1
< 0.1%
507.39 1
< 0.1%
507.34 1
< 0.1%
507.25 1
< 0.1%
507.21 1
< 0.1%

hbA
Real number (ℝ)

HIGH CORRELATION 

Distinct1439
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.804276
Minimum57.4
Maximum97.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:21.936251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57.4
5-th percentile59.2875
Q163.905
median69.49
Q376.7675
95-th percentile97.335
Maximum97.98
Range40.58
Interquartile range (IQR)12.8625

Descriptive statistics

Standard deviation13.008079
Coefficient of variation (CV)0.17625102
Kurtosis-0.86604757
Mean73.804276
Median Absolute Deviation (MAD)6.18
Skewness0.79693724
Sum162074.19
Variance169.21013
MonotonicityNot monotonic
2023-10-22T15:51:22.085831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.69 8
 
0.4%
97.85 6
 
0.3%
61.32 5
 
0.2%
97.37 5
 
0.2%
60.94 5
 
0.2%
74.95 5
 
0.2%
94.02 5
 
0.2%
65.04 5
 
0.2%
68.36 5
 
0.2%
97.69 4
 
0.2%
Other values (1429) 2143
97.6%
ValueCountFrequency (%)
57.4 1
< 0.1%
57.61 1
< 0.1%
57.66 1
< 0.1%
57.68 2
0.1%
57.7 2
0.1%
57.76 1
< 0.1%
57.83 1
< 0.1%
57.87 1
< 0.1%
57.89 1
< 0.1%
57.9 1
< 0.1%
ValueCountFrequency (%)
97.98 2
0.1%
97.97 2
0.1%
97.94 1
 
< 0.1%
97.93 1
 
< 0.1%
97.92 1
 
< 0.1%
97.9 2
0.1%
97.89 1
 
< 0.1%
97.88 3
0.1%
97.87 2
0.1%
97.86 2
0.1%

hbA2
Real number (ℝ)

HIGH CORRELATION 

Distinct396
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2291257
Minimum2
Maximum7.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-10-22T15:51:22.235100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.47
median2.93
Q33.4525
95-th percentile6.1325
Maximum7.19
Range5.19
Interquartile range (IQR)0.9825

Descriptive statistics

Standard deviation1.1485572
Coefficient of variation (CV)0.35568675
Kurtosis2.7034314
Mean3.2291257
Median Absolute Deviation (MAD)0.49
Skewness1.7581375
Sum7091.16
Variance1.3191837
MonotonicityNot monotonic
2023-10-22T15:51:22.380687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.66 20
 
0.9%
3.23 19
 
0.9%
2.63 19
 
0.9%
2.21 18
 
0.8%
3.42 18
 
0.8%
2.18 18
 
0.8%
2.16 17
 
0.8%
2.29 17
 
0.8%
2.24 17
 
0.8%
3.47 17
 
0.8%
Other values (386) 2016
91.8%
ValueCountFrequency (%)
2 5
 
0.2%
2.01 10
0.5%
2.02 13
0.6%
2.03 15
0.7%
2.04 13
0.6%
2.05 6
 
0.3%
2.06 16
0.7%
2.07 6
 
0.3%
2.08 8
0.4%
2.09 9
0.4%
ValueCountFrequency (%)
7.19 1
 
< 0.1%
7.18 5
0.2%
7.16 4
0.2%
7.15 4
0.2%
7.14 2
 
0.1%
7.13 5
0.2%
7.09 1
 
< 0.1%
7.07 2
 
0.1%
7.06 1
 
< 0.1%
7.05 1
 
< 0.1%

Thalassemia Type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size144.6 KiB
Beta Major
1027 
Alpha Major
623 
Beta Minor
340 
Alpha Minor
206 

Length

Max length11
Median length10
Mean length10.377505
Min length10

Characters and Unicode

Total characters22789
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlpha Major
2nd rowBeta Major
3rd rowAlpha Major
4th rowBeta Major
5th rowAlpha Major

Common Values

ValueCountFrequency (%)
Beta Major 1027
46.8%
Alpha Major 623
28.4%
Beta Minor 340
 
15.5%
Alpha Minor 206
 
9.4%

Length

2023-10-22T15:51:22.512726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T15:51:22.626062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
major 1650
37.6%
beta 1367
31.1%
alpha 829
18.9%
minor 546
 
12.4%

Most occurring characters

ValueCountFrequency (%)
a 3846
16.9%
2196
9.6%
M 2196
9.6%
o 2196
9.6%
r 2196
9.6%
j 1650
7.2%
B 1367
 
6.0%
e 1367
 
6.0%
t 1367
 
6.0%
A 829
 
3.6%
Other values (5) 3579
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16201
71.1%
Uppercase Letter 4392
 
19.3%
Space Separator 2196
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3846
23.7%
o 2196
13.6%
r 2196
13.6%
j 1650
10.2%
e 1367
 
8.4%
t 1367
 
8.4%
l 829
 
5.1%
p 829
 
5.1%
h 829
 
5.1%
i 546
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
M 2196
50.0%
B 1367
31.1%
A 829
 
18.9%
Space Separator
ValueCountFrequency (%)
2196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20593
90.4%
Common 2196
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3846
18.7%
M 2196
10.7%
o 2196
10.7%
r 2196
10.7%
j 1650
8.0%
B 1367
 
6.6%
e 1367
 
6.6%
t 1367
 
6.6%
A 829
 
4.0%
l 829
 
4.0%
Other values (4) 2750
13.4%
Common
ValueCountFrequency (%)
2196
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3846
16.9%
2196
9.6%
M 2196
9.6%
o 2196
9.6%
r 2196
9.6%
j 1650
7.2%
B 1367
 
6.0%
e 1367
 
6.0%
t 1367
 
6.0%
A 829
 
3.6%
Other values (5) 3579
15.7%

Interactions

2023-10-22T15:51:16.965201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.176831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.241687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.366691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.502676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.730556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.890007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.052398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.203687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.496369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.670895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.802142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.055756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.265858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.330275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.458226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.589260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.820692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.985532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.160437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.293069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.588934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.760422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.891715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.148332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.353413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.425827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.552518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.681834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.912211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.085053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.265228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.388451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.684556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.854963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.988293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.252900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.447987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.528392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.652869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.782329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.012721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.186172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.365716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.492007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.788794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.960489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.097750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.358415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.538502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.625460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.747376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.872633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.131362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.285996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.461248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.587810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.886128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.055020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.193322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.461520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.627258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.722017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.842888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.965416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.244090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.379017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.554554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.684871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.985092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.142237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.288665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.563043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.710849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.815566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.932476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.160234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.335675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.466857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.644947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.774681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.076121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.229339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.378685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.657591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.793406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.901138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.022056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.249795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.422231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.556387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.729502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.862460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.167655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.311909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.470231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.756143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.883978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.994722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.120636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.349320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.520756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.654998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.827359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.956976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.265217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.415440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.568781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.849705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:04.976561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.091784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.220317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.446938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.615316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.752842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:11.921293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.058733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.363155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.522529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.671945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:17.932274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.058541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.179387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.308734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.536444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.701896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.839353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.010856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.277664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.451330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.612042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.761061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:18.036836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:05.153122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:06.278962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:07.411309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:08.639024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:09.801409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:10.940882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:12.111111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:13.394880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:14.569863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:15.718562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:16.866630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-22T15:51:22.718636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2SexThalassemia Type
Age1.000-0.0590.004-0.0080.0080.017-0.010-0.002-0.0060.0120.0310.0280.0480.000
Hb-0.0591.0000.263-0.0050.019-0.0060.2720.0070.031-0.0060.0090.0330.6830.021
PCV0.0040.2631.0000.0030.025-0.0020.0670.0180.0350.028-0.0170.0150.2520.000
RBC-0.008-0.0050.0031.000-0.3320.062-0.9420.0020.014-0.022-0.056-0.0150.0430.043
MCV0.0080.0190.025-0.3321.000-0.0150.3170.007-0.018-0.0270.0030.0090.0310.000
MCH0.017-0.006-0.0020.062-0.0151.000-0.0540.004-0.0270.0020.0180.0160.0310.040
MCHC-0.0100.2720.067-0.9420.317-0.0541.0000.0060.0010.0220.0460.0220.1930.022
RDW-0.0020.0070.0180.0020.0070.0040.0061.0000.008-0.0130.0010.0170.0000.000
WBC-0.0060.0310.0350.014-0.018-0.0270.0010.0081.000-0.0110.0100.0230.0000.000
Plt0.012-0.0060.028-0.022-0.0270.0020.022-0.013-0.0111.0000.0130.0080.0000.017
hbA0.0310.009-0.017-0.0560.0030.0180.0460.0010.0100.0131.0000.2100.0000.673
hbA20.0280.0330.015-0.0150.0090.0160.0220.0170.0230.0080.2101.0000.0000.571
Sex0.0480.6830.2520.0430.0310.0310.1930.0000.0000.0000.0000.0001.0000.036
Thalassemia Type0.0000.0210.0000.0430.0000.0400.0220.0000.0000.0170.6730.5710.0361.000

Missing values

2023-10-22T15:51:18.295038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T15:51:18.485627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T15:51:18.610452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SexAgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2Thalassemia Type
0F5.998.2427.333.6352.7916.5920.9432.1411.10288.0074.032.01Alpha Major
1M10.759.0235.521.5178.2516.7156.8933.474.99262.7870.953.38Beta Major
2F29.668.9233.423.8162.2416.1223.2334.265.47416.0565.152.58Alpha Major
3M37.759.7729.793.3550.8126.6227.6530.046.04340.3359.083.43Beta Major
4M12.338.9930.914.1554.4218.3321.3036.578.25257.6865.612.52Alpha Major
5M41.688.3228.944.1978.4116.5618.8735.4910.48451.3167.953.40Beta Major
6F41.057.5823.142.5572.6526.0029.3133.196.21129.3964.402.02Alpha Major
7M22.998.8735.402.0261.4923.7738.7136.348.54438.8258.563.25Beta Major
8M20.989.7335.172.7253.4424.2032.1131.199.76173.2676.792.18Alpha Major
9F24.268.7329.982.9852.1317.0729.2730.348.38363.3062.352.93Beta Major
SexAgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2Thalassemia Type
2186F12.178.8930.781.9375.7022.5235.8930.2111.94314.7170.492.12Alpha Major
2187F13.798.3727.604.0355.4818.5819.0732.8010.25424.1672.743.62Beta Major
2188M7.879.2326.672.1679.2018.8235.8632.217.24237.7464.172.52Beta Major
2189F12.857.2623.191.7579.4721.0136.2331.5211.25439.9169.223.11Alpha Major
2190M18.919.9232.883.6834.3526.1425.2033.766.00107.0867.193.12Alpha Major
2191F27.748.2029.224.3946.6219.7418.3932.7710.33340.1865.722.06Beta Major
2192M17.929.8835.242.1284.7416.5641.1934.508.01455.0264.192.39Beta Major
2193M26.278.7332.382.3869.9020.3433.4233.308.06237.4269.542.68Beta Major
2194F7.517.7734.074.2769.5021.5618.1136.838.71136.8065.952.85Alpha Major
2195M14.588.6035.003.1668.6220.0026.0330.4411.97196.1593.955.78Beta Minor